Mapping the Diversity of Regional Characteristics Towards Sustainable Economic Strategic Area Development: A Case Study of West-East Corridor of West Sumatra Province
Bibliographic record
Abstract
There are different characteristics distinguishing a region from the others, thereby, leading to the diversities in the regional potentials and problems as well as the strategic regional development policies to be implemented. The East-West Corridor is one of the eleven provincial strategic economic areas in West Sumatra. It covers nine regencies or cities and 65 sub-districts with different characteristics and typologies and this leads to diversity in the strategies to develop this area. This study aims to determine the diversity associated with the characteristics and typologies of the strategic area of the East-West Corridor. This involved using Principal Component Analysis (PCA) analysis technique, spatial clustering analysis, and overlay analysis. Moreover, the regional characteristics and typologies were grouped based on 17 observational variables used in producing four main components including trade and tourism services, agriculture, livestock/fishery, and tourism. The results of spatial clustering analysis produced 3 clusters which are the urban, desa-kota, and rural areas while the overlay analysis produced ten regional characteristics and typologies used as the basis to make strategies and policies for each region’s development and to increase investment opportunities in the strategic area of West-East Corridor and the Province of West Sumatra in general.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".